# I2CR: Intra- and Inter-modal Collaborative Reflections for Multimodal Entity Linking

arXiv: https://arxiv.org/abs/2508.02243 · HTML: https://mlsanity.com/paper/2508.02243/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
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- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Entity-Based Knowledge Conflicts in Question Answering](https://mlsanity.com/paper/2109.05052/) (2021)
- [Generating Sequences by Learning to Self-Correct](https://mlsanity.com/paper/2211.00053/) (2022)
- [Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies](https://mlsanity.com/paper/2308.03188/) (2023)
- [RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs](https://mlsanity.com/paper/2305.08844/) (2023)
- [Cold Fusion: Training Seq2Seq Models Together with Language Models](https://mlsanity.com/paper/1708.06426/) (2017)
- [Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey](https://mlsanity.com/paper/2402.05391/) (2024)
- [WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types](https://mlsanity.com/paper/2204.06347/) (2022)
- [Multimodal Entity Linking for Tweets](https://mlsanity.com/paper/2104.03236/) (2021)
- [Entity Linking Meets Deep Learning: Techniques and Solutions](https://mlsanity.com/paper/2109.12520/) (2021)
- [Multi-Grained Multimodal Interaction Network for Entity Linking](https://mlsanity.com/paper/2307.09721/) (2023)
- [A Dual-way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking](https://mlsanity.com/paper/2312.11816/) (2023)
- [Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking](https://mlsanity.com/paper/2202.13404/) (2022)
- [DRIN: Dynamic Relation Interactive Network for Multimodal Entity Linking](https://mlsanity.com/paper/2310.05589/) (2023)
- [Optimal Transport Guided Correlation Assignment for Multimodal Entity Linking](https://mlsanity.com/paper/2406.01934/) (2024)
- [UniMEL: A Unified Framework for Multimodal Entity Linking with Large Language Models](https://mlsanity.com/paper/2407.16160/) (2024)
- [DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking](https://mlsanity.com/paper/2404.04818/) (2024)

## Abstract (arXiv)

Multimodal entity linking plays a crucial role in a wide range of applications. Recent advances in large language model-based methods have become the dominant paradigm for this task, effectively leveraging both textual and visual modalities to enhance performance. Despite their success, these methods still face two challenges, including unnecessary incorporation of image data in certain scenarios and the reliance only on a one-time extraction of visual features, which can undermine their effectiveness and accuracy. To address these challenges, we propose a novel LLM-based framework for the multimodal entity linking task, called Intra- and Inter-modal Collaborative Reflections. This framework prioritizes leveraging text information to address the task. When text alone is insufficient to link the correct entity through intra- and inter-modality evaluations, it employs a multi-round iterative strategy that integrates key visual clues from various aspects of the image to support reasoning and enhance matching accuracy. Extensive experiments on three widely used public datasets demonstrate that our framework consistently outperforms current state-of-the-art methods in the task, achieving improvements of 3.2%, 5.1%, and 1.6%, respectively. Our code is available at https://github.com/ziyan-xiaoyu/I2CR/.